Evolving Recurrent Networks Using Genetic Programming

ABSTRACT

A system and method for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes representing a neural network as a unique tree structure; an assembly module that assembles N RNN layers; an evolution module that evolves the H candidate RNN nodes of each respective RNN layer; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of priority to: U.S. Provisional Patent Application No. 62/627,161, titled “From Nodes to Networks: Evolving Recurrent Neural Networks,” filed Feb. 6, 2018; U.S. Provisional Patent Application No. 62/627,658, titled “From Nodes to Networks: Evolving Recurrent Neural Networks,” filed Feb. 7, 2018; U.S. Provisional Patent Application No. 62/672,200 for “Evolving Recurrent Networks Using Genetic Programming” filed May 16, 2018; and U.S. Provisional Patent Application No. 62/598,409 for “Evolving Multitask Neural Network Structure” filed on Dec. 13, 2018, each of which is incorporated herein by reference in their entireties.

INCORPORATIONS

The following materials are incorporated by reference as if fully set forth herein: paper entitled “FROM NODES TO NETWORKS: EVOLVING RECURRENT NEURAL NETWORKS,” by Aditya Rawal and Risto Miikkulainen, GECCO, Jul. 15-19, 2018, Kyoto, Japan; R. Mundra, R. Socher, “CS 224D: Deep Learning for NLP, Lecture Notes: Part III”, Spring 2015; M. Mohammadi, R. Mundra, R. Socher, “CS 224D: Deep Learning for NLP, Lecture Notes: Part IV”, Spring 2015; F. Chaubard, R. Socher, “CS 224D: Deep Learning for NLP, Lecture Notes: Part V”, Spring 2015; “Evolving deep neural networks”, by R. Miikkulainen, J. Liang, E. Meyerson, et. al. arXiv preprint arXiv:1703.00548 (2017); G. Zilly, Rupesh Kumar Srivastava, Jan Koutnik, and Jürgen Schmidhuber, “Recurrent Highway Networks” CoRR abs/1607.03474 (2016), Arxiv:1607.03474; J. Z. Liang, E. Meyerson, and R. Miikkulainen, “Evolutionary Architecture Search For Deep Multitask Networks” GECCO, Jul. 15-19, 2018, Kyoto, Japan; U.S. Nonprovisional patent application Ser. No. 15/794,913, titled “COOPERATIVE EVOLUTION OF DEEP NEURAL NETWORK STRUCTURES”, filed on Oct. 26, 2017; U.S. Nonprovisional patent application Ser. No. 15/794,905, titled “EVOLUTION OF DEEP NEURAL NETWORK STRUCTURES”, filed on Oct. 26, 2017; U.S. Pat. No. 8,768,811 titled “CLASS-BASED DISTRIBUTED EVOLUTIONARY ALGORITHM FOR ASSET MANAGEMENT AND TRADING” and U.S. Pat. No. 9,466,023 titled “DATA MINING TECHNIQUE WITH FEDERATED EVOLUTIONARY COORDINATION;” and U.S. Nonprovisional patent application Ser. No. 15/915,028, titled “ASYNCHRONOUS EVALUATION STRATEGY FOR EVOLUTION OF DEEP NEURAL NETWORKS” filed on Mar. 3, 2018.

FIELD OF THE TECHNOLOGY DISCLOSED

The technology disclosed is directed to artificial intelligence type computers and digital data processing systems and corresponding data processing methods and products for emulation of intelligence (i.e., knowledge based systems, reasoning systems, and knowledge acquisition systems); and including systems for reasoning with uncertainty (e.g., fuzzy logic systems), adaptive systems, machine learning systems, and artificial neural networks. The technology disclosed generally relates to evolving deep neural networks, and, in particular, relates to different types of architectures that can be implemented for evolving deploying deep neural networks.

BACKGROUND

The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.

Neuroevolution is a recent paradigm in the area of evolutionary computation focused on the evolution of co-adapted individuals with subcomponents without external interaction. In neuroevolution, a number of species are evolved together. The cooperation among the individuals and/or the subcomponents is encouraged by rewarding the individuals and/or the subcomponents based on how well they cooperate to solve a target problem. The work on this paradigm has shown that evolutionary models present many interesting features, such as specialization through genetic isolation, generalization and efficiency. Neuroevolution approaches the design of modular systems in a natural way, as the modularity is part of the model. Other models need some a priori knowledge to decompose the problem by hand. In many cases, either this knowledge is not available or it is not clear how to decompose the problem.

However, conventional neuroevolution techniques converge the population such that the diversity is lost and the progress is stagnated. Also, conventional neuroevolution techniques require too many parameters to be optimized simultaneously (e.g., thousands and millions of weight values at once). In addition, the deep learning structures used by conventional neuroevolution techniques are excessively large and thus difficult to optimize.

Therefore, an opportunity arises to provide improved systems and methods for cooperatively evolving deep neural network structures.

SUMMARY OF THE EMBODIMENTS

The technology disclosed relates to evolving a deep neural network based solution to a provided problem.

In a first exemplary embodiments, a computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting a candidate RNN node from the pool of candidate RNN nodes, the candidate RNN node being selected such that each respective RNN layer includes the selected candidate RNN node, N being a predetermined integer that is greater than 1, and replicating the selected candidate RNN node within each respective RNN layer M−1 times, such that each respective RNN layer includes M candidate RNN nodes, M being a predetermined integer that is greater than 1; an evolution module that evolves, for each respective RNN layer, the M candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the M candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

In a second exemplary embodiment, a computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting a candidate RNN node from the pool of candidate RNN nodes, the candidate RNN nodes being selected such that each respective RNN layer includes the selected candidate RNN node, N being a predetermined integer that is greater than 1, and replicating the selected candidate RNN node within each respective RNN layer M−1 times, such that each respective RNN layer includes M candidate RNN nodes, M being a predetermined integer that is greater than 1; an evolution module that evolves, for each respective RNN layer, the M candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the M candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that: evaluates a performance of each candidate RNN node of each layer using validation data for X epochs to obtain perplexity of each candidate RNN node at X epochs, X being an integer that is greater than 1, implements one or more long short-term memory (LSTM) neural network trained to predict perplexity of each candidate RNN node at Y epochs, given the obtained perplexity at X epochs as an input, Y being an integer and at least 4 times greater than X, and assigns a fitness value to each candidate RNN node in dependence upon the predicted perplexity; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

In a third exemplary embodiment, computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting, for each respective RNN layer of the N RNN layers, a predetermined number H of candidate RNN nodes from the pool of candidate RNN nodes, the H candidate RNN nodes being selected such that each of the H candidate RNN nodes has a unique structure, N being a predetermined integer that is greater than 1, and replicating the H candidate RNN nodes within each respective RNN layer a certain number of times, such that each RNN layer includes the same number of candidate RNN nodes; an evolution module that evolves, for each respective RNN layer, the H candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the H candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

In a fourth exemplary embodiment, a computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem, the system comprising: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting, for each respective RNN layer of the N RNN layers, a predetermined number of H candidate RNN node from the pool of candidate RNN nodes, the H candidate RNN nodes being selected such that each of the H candidate RNN nodes has a unique structure, N being a predetermined integer that is greater than 1, and replicating the H candidate RNN node within each respective RNN layer a certain number of times, such that each RNN layer includes the same number of candidate RNN nodes; an evolution module that evolves, for each respective RNN layer, the H candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the H candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that: evaluates a performance of each candidate RNN node of each RNN layer using validation data for X epochs to obtain perplexity of each candidate RNN node at X epochs, X being an integer that is greater than 1, implements one or more long short-term memory (LSTM) neural network trained to predict perplexity of each candidate RNN node at Y epochs, given the obtained perplexity at X epochs as an input, Y being an integer and at least 4 times greater than X, and assigns a fitness value to each candidate RNN node in dependence upon the predicted perplexity; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to like parts throughout the different views. Also, the drawings are not necessarily to scale, with an emphasis instead generally being placed upon illustrating the principles of the technology disclosed. In the following description, various implementations of the technology disclosed are described with reference to the following drawings, in which:

FIG. 1 illustrates one implementation of a supermodule identifying a plurality of supermodule hyperparameters that further identify a plurality of modules in the supermodule and interconnections among the modules in the supermodule.

FIG. 2 depicts one implementation of an example supermodule identifying respective values for a plurality of supermodule hyperparameters that further identify a plurality of modules in the supermodule and interconnections among the modules in the supermodule.

FIG. 3 depicts one implementation of an example deep neural network structure generated in dependence upon the example supermodule depicted in FIG. 2.

FIG. 4 depicts another implementation of an example deep neural network structure generated in dependence upon an example supermodule generated by the technology disclosed.

FIG. 5 illustrates one implementation of a blueprint identifying a plurality of blueprint hyperparameters that further identify a plurality of supermodules in the blueprint and interconnections among the supermodules in the blueprint.

FIG. 6 depicts one implementation of an example blueprint that identifies respective values for a plurality of blueprint hyperparameters which further identify a plurality of supermodules in the blueprint and interconnections among the supermodules in the blueprint.

FIG. 7 illustrates one implementation of instantiation of a candidate blueprint as multiple blueprint instantiations, and identifying, from submodule subpopulations, respective values for each of the supermodules identified in each of the blueprint instantiations of the blueprint.

FIG. 8 depicts one implementation of instantiation of two candidate blueprints with identical values for at least one supermodule.

FIG. 9 shows one implementation of a training system that can be used to cooperatively evolve the disclosed blueprints and supermodules. In implementations, various functionalities of the training system, such as speciation, evaluation, competition, and procreation apply equivalently to the disclosed blueprints and supermodules.

FIG. 10A illustrates a homogeneous RNN of two layers.

FIG. 10B illustrates an implementation of a heterogeneous RNN layer.

FIG. 10C illustrates that a recurrent node can be represented as a tree structure that can be evolved using genetic programming.

FIG. 10D illustrates homologous crossover in genetic programming.

FIG. 10E illustrates an implementation of a Hall of Shame.

FIG. 11 illustrates various modules that can be used to implement the functionality of the training system in FIG. 9. In particular, FIG. 11 shows a first evolution at the level of blueprints that comprise the supermodules and a second evolution at the level of supermodules. The first and second evolutions occur in parallel.

FIG. 12 is a simplified block diagram of a computer system that can be used to implement either or both of the training system and the production system of the technology disclosed.

DETAILED DESCRIPTION

The following discussion is presented to enable any person skilled in the art to make and use the technology disclosed, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed implementations will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other implementations and applications without departing from the spirit and scope of the technology disclosed. Thus, the technology disclosed is not intended to be limited to the implementations shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

INTRODUCTION

Evolutionary algorithms are a promising approach for optimizing highly complex systems such as deep neural networks, provided fitness evaluations of the networks can be parallelized. However, evaluation times on such systems are not only long but also variable, which means that many compute clients (e.g., worker nodes) are idle much of the time, waiting for the next generation to be evolved.

The technology disclosed proposes various architectures that can be implemented to that increase throughput of evolutionary algorithms and provide better results.

Terminology

Module: As used herein, the term “module” refers to a processor that receives information characterizing input data and generates an alternative representation and/or characterization of the input data. A neural network is an example of a module. Other examples of a module include a multilayer perceptron, a feed-forward neural network, a recursive neural network, a recurrent neural network, a deep neural network, a shallow neural network, a fully-connected neural network, a sparsely-connected neural network, a convolutional neural network that comprises a fully-connected neural network, a fully convolutional network without a fully-connected neural network, a deep stacking neural network, a deep belief network, a residual network, echo state network, liquid state machine, highway network, maxout network, long short-term memory (LSTM) network, recursive neural network grammar (RNNG), gated recurrent unit (GRU), pre-trained and frozen neural networks, and so on. Yet other examples of a module include individual components of a convolutional neural network, such as a one-dimensional (1D) convolution module, a two-dimensional (2D) convolution module, a three-dimensional (3D) convolution module, a feature extraction module, a dimensionality reduction module, a pooling module, a subsampling module, a batch normalization module, a concatenation module, a classification module, a regularization module, and so on. In implementations, a module comprises learnable submodules, parameters, and hyperparameters that can be trained by back-propagating the errors using an optimization algorithm. The optimization algorithm can be based on stochastic gradient descent (or other variations of gradient descent like batch gradient descent and mini-batch gradient descent). Some examples of optimization algorithms used by the technology disclosed include Momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. In implementations, a module is an activation module that applies a non-linearity function. Some examples of non-linearity functions used by the technology disclosed include a sigmoid function, rectified linear units (ReLUs), hyperbolic tangent function, absolute of hyperbolic tangent function, leaky ReLUs (LReLUs), and parametrized ReLUs (PReLUs). In implementations, a module is a classification module. Some examples of classifiers used by the technology disclosed include a multi-class support vector machine (SVM), a Softmax classifier, and a multinomial logistic regressor. Other examples of classifiers used by the technology disclosed include a rule-based classifier. In implementations, a module is a pre-processing module, such as an input module, a normalization module, a patch-extraction module, and a noise-addition module. In implementations, a module is a post-processing module, such as an output module, an estimation module, and a modelling module. Two modules differ in “type” if they differ in at least one submodule, parameter, or hyperparameter. In some implementations, certain modules are fixed topology modules in which a certain set of submodules are not evolved/modified and/or only evolved/modified in certain generations, and only the interconnections and interconnection weights between the submodules are evolved.

In implementations, a module comprises submodules, parameters, and hyperparameters that can be evolved using genetic algorithms (GAs). Modules need not all include a local learning capability, nor need they all include any submodules, parameters, and hyperparameters, which can be altered during operation of the GA. Preferably some, and more preferably all, of the modules are neural networks, which can learn their internal weights and which are responsive to submodules, parameters, and hyperparameters that can be altered during operation of the GA.

Any other conventional or future-developed neural networks or components thereof or used therein, are considered to be modules. Such implementations will be readily apparent to those skilled in the art without departing from the spirit and scope of the technology disclosed.

Submodule:

As used herein, the term “submodule” refers to a processing element of a module. For example, in the case of a fully-connected neural network, a submodule is a neuron of the neural network. In another example, a layer of neurons, i.e., a neuron layer, is considered a submodule of the fully-connected neural network module. In other examples, in the case of a convolutional neural network, a kernel, a filter, a feature extractor, an activation function, a pooling operation, a subsampling operation, and a regularization operation, are each considered submodules of the convolutional neural network module. In some implementations, the submodules are considered as modules, and vice-versa.

Supermodule: As used herein, the term “supermodule” refers to a sequence, arrangement, composition, and/or cascades of one or more modules. In a supermodule, the modules are arranged in a sequence from lowest to highest or from nearest to farthest or from beginning to end or from first to last, and the information characterizing the input data is processed through each of the modules in the sequence. In some implementations, certain supermodules are fixed topology supermodules in which a certain set of modules are not evolved/modified and/or only evolved/modified in certain generations, and only the interconnections and interconnection weights between the modules are evolved. Portions of this application refer to a supermodule as a “deep neural network structure”.

Blueprint: As used herein, the term “blueprint” refers to a sequence, arrangement, composition, and/or cascades of one or more supermodules. In a blueprint, the supermodules are arranged in a sequence from lowest to highest or from nearest to farthest or from beginning to end or from first to last, and the information characterizing the input data is processed through each of the supermodules in the sequence. In some implementations, certain blueprints are fixed topology blueprints in which a certain set of supermodules are not evolved/modified and/or only evolved/modified in certain generations, and only the interconnections and interconnection weights between the supermodules are evolved.

Subpopulation:

As used herein, the term “subpopulation” refers to a cluster of items that are determined to be similar to each other. In some implementations, the term “subpopulation” refers to a cluster of items that are determined to be more similar to each other than to items in other subpopulations. An item can be a blueprint. An item can be a supermodule. An item can be a module. An item can be a submodule. An item can be any combination of blueprints, supermodules, modules, and submodules. Similarity and dissimilarity between items is determined in dependence upon corresponding hyperparameters of the items, such as blueprint hyperparameters, supermodule hyperparameters, and module hyperparameters. In implementations, a subpopulation includes just one item. In some implementations, each subpopulation is stored separately using one or more databases. In other implementations, the subpopulations are stored together as a single population and only logically clustered into separate clusters.

In some implementations, the term “subpopulation” refers to a cluster of items that are determined to have the same “type” such that items in the same cluster have sufficient similar hyperparameters and/or values for certain hyperparameters to qualify as being of the same type, but enough different hyperparameters and/or values for certain hyperparameters to not be considered as the same item. For instance, subpopulations can differ based on the type of supermodules or modules grouped in the subpopulations. In one example, a first subpopulation can include supermodules that are convolutional neural networks with fully-connected neural networks (abbreviated CNN-FCNN) and a second subpopulation can include supermodules that are fully convolutional networks without fully-connected neural networks (abbreviated FCN). Note that, in the first subpopulation, each of the supermodules has the same CNN-FCNN type and at least one different hyperparameter or hyperparameter value that gives them distinguishing identities, while grouping them in the same first subpopulation. Similarly, in the second subpopulation, each of the supermodules has the same FCN type and at least one different hyperparameter or hyperparameter value that gives them distinguishing identities, while grouping them in the same second subpopulation. In one implementation, this is achieved by representing the hyperparameters values for each of the supermodules as vectors, embedding the vectors in a vector space, and clustering the vectors using a clustering algorithm such as Bayesian, K-means, or K-medoids algorithms.

Preferably, a plurality of subpopulations is maintained at the same time. Also preferably, a plurality of subpopulations is created and/or initialized in parallel. In one implementation, the subpopulations are created by speciation. In one implementation, the subpopulations are modified by speciation. Speciation can create new subpopulations, add new items to pre-existing subpopulations, remove pre-existing items from pre-existing subpopulations, move pre-existing items from one pre-existing subpopulation to another pre-existing subpopulation, move pre-existing items from a pre-existing subpopulation to a new subpopulation, and so on. For example, a population of items is divided into subpopulations such that items with similar topologies, i.e., topology hyperparameters, are in the same subpopulation.

In implementations, for clustering items in the same subpopulation, speciation measures a compatibility distance between items in dependence upon a linear combination of the number of excess hyperparameters and disjoint hyperparameters, as well as the average weight differences of matching hyperparameters, including disabled hyperparameters. The compatibility distance measure allows for speciation using a compatibility threshold. An ordered list of subpopulations is maintained, with each subpopulation being identified by a unique identifier (ID). In each generation, items are sequentially placed into the subpopulations. In some implementations, each of the pre-existing subpopulations is represented by a random item inside the subpopulation from the previous generation. In some implementations, a given item (pre-existing or new) in the current generation is placed in the first subpopulation in which it is compatible with the representative item of that subpopulation. This way, subpopulations do not overlap. If the given item is not compatible with any existing subpopulations, a new subpopulation is created with the given item as its representative. Thus, over generations, subpopulations are created, shrunk, augmented, and/or made extinct.

In Parallel:

As used herein, “in parallel” or “concurrently” does not require exact simultaneity. It is sufficient if the evaluation of one of the blueprints begins before the evaluation of one of the supermodules completes. It is sufficient if the evaluation of one of the supermodules begins before the evaluation of one of the blueprints completes.

Identification:

As used herein, the “identification” of an item of information does not necessarily require the direct specification of that item of information. Information can be “identified” in a field by simply referring to the actual information through one or more layers of indirection, or by identifying one or more items of different information which are together sufficient to determine the actual item of information. In addition, the term “specify” is used herein to mean the same as “identify”.

In Dependence Upon:

As used herein, a given signal, event or value is “in dependence upon” a predecessor signal, event or value of the predecessor signal, event or value influenced by the given signal, event or value. If there is an intervening processing element, step or time period, the given signal, event or value can still be “in dependence upon” the predecessor signal, event or value. If the intervening processing element or step combines more than one signal, event or value, the signal output of the processing element or step is considered “in dependence upon” each of the signal, event or value inputs. If the given signal, event or value is the same as the predecessor signal, event or value, this is merely a degenerate case in which the given signal, event or value is still considered to be “in dependence upon” or “dependent on” or “based on” the predecessor signal, event or value. “Responsiveness” of a given signal, event or value upon another signal, event or value is defined similarly.

System Overview

Supermodules

FIG. 1 illustrates one implementation of a supermodule 100 identifying a plurality of supermodule hyperparameters that further identify a plurality of modules in the supermodule 100 and interconnections among the modules in the supermodule 100. The supermodule hyperparameters include supermodule global topology hyperparameters that identify a plurality of modules (e.g., module 1 to module n) in the supermodule 100 and interconnections among the modules in the supermodule 100. In one implementation, at least one of the modules in the supermodule 100 includes a neural network. In implementations, each supermodule (such as supermodule 100) has associated therewith storage for an indication of a respective supermodule fitness value.

The hyperparameters further include local topology hyperparameters, which apply to the modules and identify a plurality of submodules of the neural network and interconnections among the submodules. In some implementations, the hyperparameters further include global topology hyperparameters. In other implementations, the hyperparameters further include local topology hyperparameters. Global hyperparameters apply to and/or are configured for an entire supermodule, i.e., they apply uniformly across all the modules of a supermodule. In contrast, local hyperparameters apply to and/or are configured for respective modules in a supermodule, i.e., each module in a supermodule can have its own set of local hyperparameters, which may or may not overlap with a set of local hyperparameters of another module in the supermodule.

The “type” of a module is determined by a set of hyperparameters that identify the module. Two modules differ in “type” if they differ in at least one hyperparameter. For example, a convolution module can have the following local topology hyperparameters—kernel size and number of kernels. A fully-connected neural network module can have the following local topology parameters—number of neurons in a given neuron layer, number of neuron layers in the fully-connected neural network, and interconnections and interconnection weights between the neurons in the neural network. In implementations, two modules that have a same set of hyperparameters, but different values for some of the hyperparameters are considered to belong to the same type.

A sample set of hyperparameters according to one implementation includes the following:

Topology Operational Global Hyperparameters Number of modules, Learning rate, learning rate interconnections among decay, momentum, weight the modules, type of initialization, regularization interconnections (e.g., strength, initialization deviation, residual connections, skip input initialization deviation, connections), type of Hue shift, saturation scale, modules (e.g., residual saturation shift, value scale, blocks). value shift, pixel dropout, L2 weight decay, and fully- connected layer drop out. Local Hyperparameters For a fully-connected Learning rate, momentum, neural network module: weight initialization, and fully- the number of neurons in connected layer drop out. each neuron layer, the number of neuron layers, and the interconnections among the neurons from one neuron layer to the next. For a convolutional neural network module: kernel size, number of kernels, kernel depth, kernel stride, kernel padding, activation pooling, subsampling, pooling, and normalization. For an image preprocessing module: image shift, translation, and flipping. Blueprint Hyperparameters Number of supermodules, interconnections among the supermodules, and supermodule subpopulation for each included supermodule.

FIG. 2 depicts one implementation of an example supermodule 200 identifying respective values for a plurality of supermodule hyperparameters that further identify a plurality of modules in the example supermodule 200 and interconnections among the modules in the example supermodule 200. The supermodule global topology hyperparameters of example supermodule 200 identify four modules, namely a first hidden module that is a first convolution module, a second hidden module that is a second convolution module, an input module, and an output module. The supermodule global topology hyperparameters of example supermodule 200 also identify interconnections among the four modules using edges 1 to 4. Edge 1 identifies that the input module feeds forward to the first hidden module. Edge 2 identifies that the input module also feeds forward to the second hidden module. Edge 3 identifies that the second hidden module also feeds forward to the output module. Edge 4 identifies that the first hidden module also feeds forward to the output module.

Also, in FIG. 2, the global operational hyperparameters, namely learning rate, momentum, and LR decay apply uniformly to all the modules in FIG. 2. Further, the first convolution module has different respective hyperparameter values for the kernel size and the number of kernels than that of the second convolution module. Furthermore, they have different values for the local operational hyperparameter called dropout rate.

In other implementations, different encodings, representations, or structures can be used to identify a module and its interconnections in the disclosed supermodules. For example, encodings, representations, and/or structures equivalent to encodings, representations, and/or structures disclosed in the academic paper “Kenneth O. Stanley and Risto Miikkulainen, “Evolving neural networks through augmenting topologies,” Evolutionary Computation, 10(2):99-127, 2002” (hereinafter “NEAT”) can be used, which is incorporated by reference for all purposes as if fully set forth herein. In NEAT, the disclosure pertained to evolution of individual neural networks of a single type. In contrast, this application discloses evolution of supermodules that include a plurality of neural networks of varying types.

FIG. 3 depicts one implementation of an example deep neural network structure 300 generated in dependence upon the example supermodule depicted in FIG. 2. Example deep neural network structure 300 includes an input module that feeds forward to the first and second convolution modules. The first and second convolution modules feed forward to the output module. The hyperparameters in FIG. 2 are applied accordingly to the example deep neural network structure 300 and respective modules.

FIG. 4 depicts another implementation of an example deep neural network structure 400 generated in dependence upon an example supermodule generated by the technology disclosed. The corresponding example supermodule is not shown for simplicity's sake. The supermodule global topology hyperparameters of the corresponding example supermodule identify five modules, namely a convolution module 1, a batch normalization module 2, a pooling module 3, a fully-connected neural network module 4, and a classification module 5. The local topology hyperparameters of the corresponding example supermodule apply on a module-by-module basis. For example, the batch normalization module 2 has two local topology hyperparameters—scaling and shifting. The pooling module 3 has one local topology hyperparameter—maxpooling. The fully-connected neural network module 4 has the following local topology hyperparameters—number of neurons in each neuron layer, number of neuron layers (e.g., L1, L2, Ln), and interconnections and interconnection weights between the neurons. The classification module 5 has one local topology hyperparameters—softmax. In implementations, the kernels, the neurons, the neuron layers, the interconnections, and the interconnection weights are considered to be submodules of the respective modules.

Blueprints

FIG. 5 illustrates one implementation of a blueprint 500 identifying a plurality of blueprint hyperparameters that further identify a plurality of supermodules (e.g., supermodule 1 to supermodule n) in the blueprint and interconnections among the supermodules in the blueprint. Blueprint 500 provides a deep neural network based solution to a provided problem. The blueprint topology hyperparameters identify a number of included supermodules in the blueprint 500, interconnections or interconnects among the included supermodules, and the supermodule subpopulations (e.g., subpopulations 1 to n) from which to retrieve each of the included supermodules. Each blueprint (such as blueprint 500) has associated therewith storage for an indication of a respective blueprint fitness value.

In some implementations, as shown in FIG. 5, a blueprint (such as blueprint 500) includes repetitive supermodules, i.e., supermodules slots in the same blueprint that are identified, selected, filled, and/or retrieved by accessing the same supermodule subpopulation. In some implementations, accessing a same supermodule subpopulation results in different supermodule slots of a particular blueprint being filled with same supermodule type. In blueprint 500, the first and the third supermodule slots are filled with supermodules retrieved from the same supermodule subpopulation 1. In such a case, the retrieved supermodules may have the same type but at least one different hyperparameter or hyperparameter value that distinguishes them, according to some implementations. In other implementations, because the supermodules are identified, selected, filled, and/or retrieved randomly from the supermodule subpopulations, it can be the case that the same supermodule is identified, selected, filled, and/or retrieved for multiple supermodule slots in a given blueprint.

FIG. 6 depicts one implementation of an example blueprint 600 that identifies respective values for a plurality of blueprint hyperparameters, which in turn identify a plurality of supermodules in the example blueprint 600 and interconnections among the supermodules in the example blueprint 600. In the example shown in FIG. 6, the blueprint global topology hyperparameters of the example blueprint 600 identify five supermodules, namely a fully convolutional network supermodule without a fully-connected neural network, a recurrent or recursive neural network (RNN) supermodule, a fully-connected neural network supermodule, a convolutional neural network (CNN) supermodule with a fully-connected neural network, and a hybrid supermodule that combines a RNN with a CNN into a single RNN-CNN supermodule.

The blueprint global topology hyperparameters of the example blueprint 600 also identify interconnections or interconnects among the five supermodules. In implementations, the interconnections define the processing of data in the example blueprint 600 from the lowest supermodule to the highest supermodule or from the nearest supermodule to the farthest supermodule or from the beginning supermodule to the end supermodule or from the first supermodule to the last supermodule.

FIG. 7 illustrates one implementation of instantiation of a candidate blueprint as multiple blueprint instantiations, and identifying, from submodule subpopulations, respective values for each of the supermodules identified in each of the blueprint instantiations of the blueprint. In implementations of the technology disclosed, prior to training, a blueprint is instantiated into multiple blueprint instantiations. Each instantiation of a given blueprint has the same topology but different instances of each supermodule from the blueprint-specified subpopulation. In other words, all instantiations of a particular blueprint identify the same blueprint global topology hyperparameters, for example, a number of supermodules to be included in the instantiations, interconnections or interconnects among the supermodules to be included in the instantiations, and the supermodule subpopulations from which to retrieve each of the supermodules to be included in the instantiations.

In the example shown in FIG. 7, a first blueprint has two instantiations—instantiation X and instantiation Y. Instantiations X and Y of the first blueprint have the same blueprint global topology hyperparameters such that they both include three supermodules. More importantly, the supermodules in the respective supermodule sets identified by the instantiations X and Y are to be retrieved from the same corresponding subpopulations 1, 2, and 3. Additionally, in blueprint instantiations X and Y, the comprising supermodules are interconnected in the same order.

Blueprint instantiations of a particular blueprint differ in the way that, even though, to fill their respective supermodule slots, they identify a same subpopulation, they retrieve a different supermodule from the same subpopulation to fill their respective supermodule slots. In the example shown in FIG. 7, each of the subpopulations 1, 2, and 3 includes many supermodules (SPs). The supermodules in a subpopulation have the same type (e.g., CNN), but they also have at least one different hyperparameter or hyperparameter value that makes them separate SPs within the same subpopulation. In FIG. 7, different SPs within the same subpopulation are identified by the varying number of apostrophes symbols (′) next to the SPs. Accordingly, in one example, all the SPs in subpopulation 1 can be recurrent or recursive neural networks, all the SPs in subpopulation 2 can be deep belief networks, and all the SPs in subpopulation 3 can be residual networks. In the example shown in FIG. 7, supermodule 1 of first blueprint's instantiation X receives “SP1′” from subpopulation 1 and supermodule 1 of first blueprint's instantiation Y receives “SP1″″″” from subpopulation 1; supermodule 2 of first blueprint's instantiation X receives “SP2″″” from subpopulation 2 and supermodule 2 of first blueprint's instantiation Y receives “SP2′″” from subpopulation 2; and supermodule 3 of first blueprint's instantiation X receives “SP3′″” from subpopulation 3 and supermodule 3 of first blueprint's instantiation Y receives “SP3″” from subpopulation 3.

Blueprint Fitness

In implementations, a particular blueprint's fitness is the average performance of all the tested instantiations of the particular blueprint. Using the example shown in FIG. 7, the fitness of the first blueprint is the average of the fitness of its instantiation X and the fitness of its instantiation Y.

FIG. 8 depicts one implementation of instantiation of two candidate blueprints with identical values for at least one supermodule. In one implementation, two blueprints are considered different blueprints, and not just instantiations of a same blueprint, when they differ based on the type of supermodules identified by them. For example, when a first blueprint identifies a supermodule type not identified by a second blueprint. In another implementation, two blueprints are considered different blueprints, and not just instantiations of a same blueprint, when they differ based on the subpopulations identified by them for filling the supermodule slots. For example, when a first blueprint identifies a subpopulation not identified by a second blueprint. In yet another implementation, two blueprints are considered different blueprints, and not just instantiations of a same blueprint, when they differ based on the number of supermodules identified by them (even though they do not differ based on the supermodule types and the subpopulations). In yet further implementation, two blueprints are considered different blueprints, and not just instantiations of a same blueprint, when they differ based on the interconnections, arrangement and/or sequence of the supermodules identified by them (even though they do not differ based on the supermodule types, the subpopulations, and the number of supermodules).

In the example shown in FIG. 8, first blueprint's instantiation X and second blueprint's instantiation Z are considered different blueprints, and not just instantiations of a same blueprint, at least because they differ based on the supermodule subpopulations they identify to fill their respective supermodule slots. The first blueprint identifies subpopulation 2 to fill its second supermodule slot. In contrast, the second blueprint identifies subpopulation 5 to fill its second supermodule slot.

Note that, in FIG. 8, a particular supermodule “SP1′” from a same subpopulation 1 is identified by the two different blueprints. Also in FIG. 8, note that another particular supermodule “SP3′″” from a same subpopulation 3 is identified by the two different blueprints. This is different from the case of different instantiations of a same blueprint, which do not identify a same particular supermodule from a given subpopulation. Thus, in some implementations of the technology, a same particular supermodule from a given subpopulation can be identified by different blueprints.

If a given supermodule has multiple occurrences in a particular blueprint, then, in some implementations, multiple instances of the given supermodule's fitness are incorporated when determining the average performance of the particular blueprint. In other implementations, only one instance of the given supermodule's fitness is used.

Supermodule Fitness

In implementations, a particular supermodule's fitness is the average performance of all the different blueprints in which the particular supermodule is identified and tested. Using the example shown in FIG. 8, the fitness of the supermodule “SP1′” is the average of the respective fitnesses of the first and second blueprints because supermodule “SP1′” is identified in both of the different blueprints. Similarly, the fitness of the supermodule “SP3′″” is the average of the respective fitnesses of the first and second blueprints because supermodule “SP3′″” is identified in both of the different blueprints.

FIG. 8 also shows that the first blueprint's instantiation X includes repetitive supermodules, i.e., supermodule slots in a given blueprint that are identified, selected, filled, and/or retrieved with supermodules from a same supermodule subpopulation. In the example used in FIG. 8, supermodules 1 and 2 of the first blueprint's instantiation X are identified, selected, filled, and/or retrieved from a same supermodule subpopulation 1 SP1.

In some implementations, a particular supermodule is used to fill multiple supermodule slots of the same blueprint. In such a case, the fitness calculation of such a repetitive supermodule includes accounting for the duplicate performance during the averaging across performance of different blueprints that included the particular supermodule. In other implementations, the duplicate performance is ignored during the averaging.

Training System

FIG. 9 shows one implementation of a training system 900 that can be used to cooperatively evolve the disclosed blueprints and supermodules. In implementations, various functionalities of the training system 900, such as speciation, evaluation, competition, and procreation apply equivalently to the disclosed blueprints and supermodules. In this section, the term “genome” is used to refer to both a blueprint and a supermodule such that, in some implementations, processing of a genome in the training system 900 means speciating, evaluating, discarding, and procreating modules of a supermodule (and corresponding interconnections and interconnection weights), and in other implementations, processing of a genome in the training system 900 means speciating, evaluating, discarding, and procreating supermodules of a blueprint (and corresponding interconnections and interconnection weights). In this section, the two references to genome are meant to equivalently apply at the blueprint level as well as the supermodule level.

The system in FIG. 9 is divided into two portions—the training system 900 and the production system 934. The training system 900 interacts with a database 902 containing a candidate genome pool. As used herein, the term “database” does not necessarily imply any unity of structure. For example, two or more separate databases, when considered together, still constitute a “database” as that term is used herein. In one implementation, the candidate genome pool database 902 represents a blueprint genome database having a pool of candidate blueprints for solutions to a provided problem (e.g., blueprint candidate pool 1112 in FIG. 11). In one implementation, the candidate genome pool database 902 represents a candidate supermodule genome database having a pool of candidate supermodules (e.g., supermodule candidate pool 1104 in FIG. 11). The candidate genome pool database 902 includes a portion containing an elitist pool 912. In some implementations, the candidate genome pool database 902 can store information from one or more tenants into tables of a common database image to form an on-demand database service (ODDS), which can be implemented in many ways, such as a multi-tenant database system (MTDS). A database image can include one or more database objects. In other implementations, the databases can be relational database management systems (RDBMSs), object-oriented database management systems (OODBMSs), distributed file systems (DFS), no-schema database, or any other data storing systems or computing devices.

The training system 900 operates according to fitness function 904, which indicates to the training system 900 how to measure the fitness of a genome. The training system 900 optimizes for genomes that have the greatest fitness, however fitness is defined by the fitness function 904. The fitness function 904 is specific to the environment and goals of the particular application. For example, the fitness function may be a function of the predictive value of the genome as assessed against the training data 918—the more often the genome correctly predicts the result represented in the training data, the more fit the genome is considered. In a financial asset trading environment, a genome might provide trading signals (e.g., buy, sell, hold current position, exit current position), and fitness may be measured by the genome's ability to make a profit, or the ability to do so while maintaining stability, or some other desired property. In the healthcare domain, a genome might propose a diagnosis based on patient prior treatment and current vital signs, and fitness may be measured by the accuracy of that diagnosis as represented in the training data 918. In the image classification domain, the fitness of a genome may be measured by the accuracy of the identification of image labels assigned to the images in the training data 918.

In one implementation, the genomes in candidate genome pool database 902 are stored and managed by conventional database management systems (DBMS), and are accessed using SQL statements. Thus, a conventional SQL query can be used to obtain, for example, the fitness function 904 of the genomes. New genomes can be inserted into the candidate genome pool database 902 using the SQL “insert” statement, and genomes being discarded can be deleted using the SQL “delete” statement. In another implementation, the genomes in the candidate genome pool database 902 are stored in a linked list. In such an implementation insertion of a new genome can be accomplished by writing its contents into an element in a free list, and then linking the element into the main linked list. Discarding of genomes involves unlinking them from the main linked list and re-linking them into the free list.

The production system 934 operates according to a production genome pool 932 in another database. The production system 934 applies these genomes to production data, and produces outputs, which may be action signals or recommendations. In the financial asset trading environment, for example, the production data may be a stream of real time stock prices and the outputs of the production system 934 may be the trading signals or instructions that one or more of the genomes in the production genome pool 932 outputs in response to the production data. In the healthcare domain, the production data may be current patient data, and the outputs of the production system 934 may be a suggested diagnosis or treatment regimen that one or more of the genomes in the production genome pool 932 outputs in response to the production data via the production system 934. In the image classification domain, the production data may be user-selected products on a website, and the outputs of the production system 934 may be recommendations of other products that one or more of the genomes in the production genome pool 932 outputs in response to the production data. The production genome pool 932 is harvested from the training system 900 once or at intervals, depending on the implementation. Preferably, only genomes from the elitist pool 912 are permitted to be harvested. In an implementation, further selection criteria are applied in the harvesting process.

In implementations, the production system 934 is a server that is improved by the evolved genomes in the production genome pool 932. In such an implementation, the production system 934 is a server that is responsible for implementing machine learning based solutions to a provided problem. Since the evolved genomes identify hyperparameters that have high fitness function, they improve, for example, the accuracy, the processing speed, and various computations of the production system 934 during its application of machine learning based solutions. In one example, the evolved genomes identify deep neural network structures with higher learning rates. Such specialized structures can be implemented at the production system 934 to provide sub-second responses to queries seeking real-time machine learned answers to a provided problem. In another example, the superior kernels, scaling, and shifting hyperparameters of a convolutional neural network, the superior neurons and neuron layers of a fully-connected neural network, and the superior interconnection weights between the kernels and between the neurons are used to enhance the accuracy of the production system 934 for real-time tasks such as image classification, image recognition, gesture recognition, speech recognition, natural language processing, multivariate testing, pattern recognition, online media recommendation, and so on. The result is an improved production system 934 with enhanced functionalities.

The controlled system 944 is a system that is controlled automatically by the signals from the production system 934. In the financial asset trading environment, for example, the controlled system 944 may be a fully automated brokerage system which receives the trading signals via a computer network (not shown) and takes the indicated action. In a webpage testing environment, for example, the controlled system 944 is a product distribution e-warehouse (e.g., Amazon.com™) that receives the signals via a computer network (not shown) and takes appropriate transactional and delivery actions. Depending on the application environment, the controlled system 944 may also include mechanical systems such as engines, air-conditioners, refrigerators, electric motors, robots, milling equipment, construction equipment, or a manufacturing plant.

The candidate genome pool database 902 is initialized by a population initialization module, which creates an initial set of candidate genomes in the population. These genomes can be created randomly, or in some implementations a priori knowledge is used to seed the first generation. In another implementation, genomes from prior runs can be borrowed to seed a new run. At the start, all genomes are initialized with a fitness function 904 that are indicated as undefined.

A speciating module clusters the genomes into subpopulations based on hyperparameter comparison, as discussed in detail in other portions of this application.

A candidate testing module then proceeds to train the genomes and corresponding modules and/or supermodules in the candidate genome pool database 902 on the training data 918. In one implementation, it does so by back-propagating the errors using an optimization algorithm, as discussed above. Once trained, the candidate testing module then tests the genomes and corresponding deep neural network structures in the candidate genome pool database 902 on the validation data 928. Each genome undergoes a battery of tests or trials on the validation data 928, with each trial testing the genome on one sample. In one implementation, each battery might consist of only a single trial. Preferably, however, a battery of tests is much larger, for example on the order of 1000 trials. Note there is no requirement that all genomes undergo the same number of trials. After the tests, a candidate testing module updates the fitness estimate associated with each of the genomes tested.

In an implementation, the fitness estimate may be an average of the results of all trials of the genome. In this case the “fitness estimate” can conveniently be indicated by two numbers: the sum of the results of all trials of the genome, and the total number of trials that the genome has experienced. The latter number may already be maintained as the experience level of the genome. The fitness estimate at any particular time can then be calculated by dividing the sum of the results by the experience level of the genome. In an implementation such as this, “updating” of the fitness estimate can involve merely adding the results of the most recent trials to the prior sum.

In one implementation a Meta-LSTM 946 can be used to estimate the performance of one or more RNN(s) without running the validation data 928 through the one or more RNN(s) 40 times (epochs).

Next, the competition module updates the candidate genome pool database 902 contents in dependence upon the updated fitness estimates. In discarding of genomes in dependence upon their updated fitness values, a competition module compares the updated fitness values of genomes only to other genomes in the same subpopulation, in some implementations. The operation of the competition module is described in more detail below, but briefly, the competition module discards genomes that do not meet the minimum genome fitness of their respective subpopulations, and discards genomes that have been replaced in a subpopulation by new entrants into that subpopulation. In other implementations, the competition module discards genomes that do not meet the minimum baseline genome fitness or whose “genome fitness” relatively lags the “genome fitness” of similarly tested genomes. Candidate genome pool database 902 is updated with the revised contents. In other implementations, all remaining genomes form the elitist pool 912. In yet other implementations, the elitist pool 912 is a subset of the remaining genomes.

After the candidate genome pool database 902 has been updated, a procreation module evolves a random subset of them. Only genomes in the elitist pool 912 are permitted to procreate. Any conventional or future-developed technique can be used for procreation. In an implementation, conditions, outputs, or rules from parent genomes are combined in various ways to form child genomes, and then, occasionally, they are mutated. The combination process for example may include crossover—i.e., exchanging conditions, outputs, or entire rules between parent genomes to form child genomes. New genomes created through procreation begin with performance metrics that are indicated as undefined. Preferably, after new genomes are created by combination and/or mutation, the parent genomes are retained. In this case the parent genomes also retain their fitness function 904, and remain in the elitist pool 912. In another implementation, the parent genomes are discarded.

In implementations, the competition module manages the graduation of genomes from the pool 902 to the elitist pool 912. This process can be thought of as occurring one genome at a time, as follows. First, a loop is begun through all genomes from whom the fitness function 904 have been updated since the last time the competition module was executed. If the fitness function 904 for a current genome is still below a baseline genome fitness or sufficiently lags relative genome fitness of other genomes, then the genome is discarded and the next one is considered. If the fitness function 904 for the current genome is above a baseline genome fitness or relatively on par with genome fitness of other genomes, then the genome is added to the elitist pool 912. The process then moves on to consider the next genome in sequence.

In implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between two selected parent genomes such that for all new genomes formed by crossover between two selected parent genomes, the two selected parent genomes share a single subpopulation. In one implementation, the procreation module, in forming new genomes, incrementally complexifies the minimal structure modules and/or supermodules in each candidate genome. In some implementations, the incremental complexification comprises adding new submodules and/or modules in the minimal structure modules and/or supermodules using mutation. In another implementation, the procreation module forms new genomes in dependence upon a respective set of at least one parent genome with at least one minimal structure module and/or supermodule, and certain new genomes identify global topology hyperparameter values identifying new complex submodules and/or modules formed in dependence upon the minimal structure module and/or supermodule using crossover. In yet another implementation, the procreation module forms new genomes in dependence upon a respective set of at least one parent genome with at least one minimal structure module and/or supermodule, and at least one of the new genomes identifies values for global topology hyperparameters identifying new complex submodules and/or modules formed in dependence upon the minimal structure module and/or supermodule using crossover.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between the global topology hyperparameter values of two selected parent genomes. In one implementation, the crossover between the global topology hyperparameter values of the two selected parent genomes includes a crossover between modules and/or supermodules of the parent genomes. In another implementation, the crossover between the global topology hyperparameter values of the two selected parent genomes includes a crossover between interconnections among modules and/or supermodules of the parent genomes.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between the local topology hyperparameter values of respective modules and/or supermodules of two selected parent genomes. In one implementation, the crossover between the local topology hyperparameter values of the two selected parent genomes includes a crossover between submodules and/or modules of the parent genomes. In another implementation, the crossover between the local topology hyperparameter values of the two selected parent genomes includes a crossover between interconnections among submodules and/or modules of the parent genomes.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between two selected parent genomes such that at least a first selected parent genome includes certain mismatching blueprint, supermodule, and/or module hyperparameters. In such an implementation, the procreation module forms the new genomes by selecting the mismatching blueprint, supermodule, and/or module hyperparameters when the first selected parent genome has a higher fitness value.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between two selected parent genomes such that at least one selected parent genome includes certain mismatching blueprint, supermodule, and/or module hyperparameters. In such an implementation, the procreation module forms the new genomes by randomly selecting at least one of the mismatching blueprint, supermodule, and/or module hyperparameters.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between the global operational hyperparameter values of two selected parent genomes.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by crossover between the local operational hyperparameter values of respective modules and/or supermodules of two selected parent genomes.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which adds a new interconnection between two pre-existing modules and/or supermodules.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which adds new interconnections between two pre-existing submodules and/or modules.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which adds a new module to a pre-existing genome.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which adds new interconnections to and from the new module.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which adds a new submodule to a pre-existing module and/or supermodule.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which adds new interconnections to and from the new submodule and/or module.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which deletes a pre-existing module and/or supermodule from a pre-existing genome.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which deletes pre-existing interconnections to and from the deleted module and/or supermodule.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which deletes a pre-existing submodule from a pre-existing module and/or supermodule.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which deletes pre-existing interconnections to and from the deleted submodule and/or module.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which changes weights of pre-existing interconnections between the modules and/or supermodules.

In some implementations, the procreation module, in forming new genomes, forms certain new genomes by mutation which changes weights of pre-existing interconnections between the submodules and/or modules.

After procreation, the speciation module and the candidate testing module operate again on the updated candidate genome pool database 902. The process continues repeatedly. In some implementations, a control module iterates the candidate testing module, the competition module, and the procreation module until after the competition module yields a candidate pool of genomes not yet discarded but which satisfy a convergence condition. The convergence condition can be defined as an optimal output of the fitness function 904, according to some definition. The convergence condition may be, for example, a recognition that the candidate pool is no longer improving after each iteration.

The following pseudo code shows one implementation of the operation of the training system 900:

set_evolution_parameters create_candidate_geomes_generation_1 repeat repeat train_candidate_genomes test_candidates_genomes until candidate_genome_spread_is_sufficient or remove_criteria_is_met remove_low_performing_candidate_genomes create_new_candidate_genomes Until performance_is_sufficient or no_more_designs_are_possible

In some implementations, the genomes in the candidate genome pool database 902 pool are referred to herein as the “winning genomes”. In implementations, each iteration through the candidate testing module, the competition module, and the procreation module can produce just one winning genome or multiple winning genomes.

In some implementations, a candidate harvesting module retrieves the winning genomes from the candidate genome pool database 902 and writes them to the production genome pool database 932. In one implementation, a candidate harvesting module retrieves genomes periodically, whereas in another implementation it retrieves genomes only in response to administrator input.

Methods of Evolving Recurrent Neural Networks Using the Training System

Evolving recurrent neural networks (RNNs) is an interesting problem because the evolution requires searching the architecture of both the node and the network and because the RNN itself can be considered a deep network. Genetic Programming (GP) can be used to evolve such node architectures. In one embodiment the overall network architecture can remain fixed (i.e., constructed by repeating a single evolved node to form a layer), as illustrated in FIG. 10A. In a second embodiment, the RNN can be evolved by combining several different types of nodes into a layer to create a heterogeneous RNN layer, as illustrated in FIG. 10B. In another embodiment, a sequence model called meta-LSTM is implemented to speed up the costly evaluation of the evolution of these networks.

General Evolution of Recurrent Nodes

FIG. 10C illustrates a recurrent node can be represented as a tree structure that can be evolved using genetic programming (GP). As illustrated in FIG. 10C, a recurrent node usually has two types of outputs. The first output is the main recurrent output at a given time, represented as h(t) and the second output is a native memory cell output at a given time c(t). The value of h(t) is weighted and fed (propagated) to three locations: (1) to a higher layer of the network at the same time step (e.g., RNN layer 2), (2) to other nodes in the network (e.g., RNN layer 1) at the next time step, and (3) to the node itself at the next time step. Before propagation, h(t) is combined with weighted activations from the previous layer, such as input word embeddings in language modeling, to generate eight node inputs (termed as base eight by Zoph et al.). In comparison, a standard LSTM node has four inputs. The native memory cell output is fed back, without weighting, only to the node itself at the next time step. The connections within a recurrent cell are not trainable by backpropagation and they all carry a fixed weight of 1.0. Thus, even without an explicit recurrent loop, the recurrent node can be represented as a tree. There are two type of elements in the tree: (1) linear activations with arity two (add, multiply), and (2) non-linear activations with arity one (tan h, sigmoid, relu).

Mutation

In this embodiment, the RNN trees can be evolved/mutated in three different ways, including: (1) mutation to randomly replace an element with an element of the same type, (2) mutation to randomly inserts a new branch at a random position in the tree (a subtree at a chosen position is used as child node of the newly created subtree); and (3) mutation to shrink the tree by choosing a branch randomly and replacing it with one of the branch's arguments (also randomly chosen). One limitation of a standard tree is that it can have only a single output: the root. This problem can be overcome by using a modified representation of a tree that consists of Modi outputs. In this approach, with some probability p (termed modirate), non-root nodes can be connected to any of the possible outputs. A higher modirate would lead to many sub-tree nodes connected to different outputs. A node is assigned modi (i.e. connected to memory cell outputs c or d) only if its sub-tree has a path from native memory cell inputs. This representation allows searching for a wide range of recurrent node structures with genetic programming.

Speciation and Crossover

One-point crossover is the most common type of crossover in GP. However, since it does not take into account the tree structure, it can often be destructive. An alternative approach, called homologous crossover, is designed to avoid this problem by crossing over the common regions in the tree. Similar tree structures in the population can be grouped into species, as is often done in NEAT. Speciation achieves two objectives: (1) it makes homologous crossover effective, since individuals within species are similar, and (2) it helps keep the population diverse, since selection is carried out separately in each species. A tree distance metric [21] is used to determine how similar the trees are: δ=(N_(unique)/N_(shared)), where Nunique is the fraction of the tree that is not shared between the two trees and Nshar ed is the fraction of the tree that is shared between the two trees. Thus two trees will have a distance of zero if their structure is the same (irrespective of the actual element types). In most GP implementations, there is a concept of the left and the right branch.

In an embodiment tree distance is computed by comparing trees after all possible tree rotations, i.e. swaps of the left and the right branch. Without such a comprehensive tree analysis, two trees that are mirror images of each other might end up into different species. This approach reduces the search space by not searching for redundant trees. It also ensures that crossover can be truly homologous, as illustrated in FIG. 10D. In FIG. 10D, which illustrates homologous crossover in GP, the two trees on the top look different but in-fact they are almost mirror images of each other. These two trees will therefore belong in the same species. The line drawn around the trees marks the homologous regions between the two. A crossover point is randomly selected and one point crossover is performed. The bottom two networks are the resultant offsprings.

The structural mutations in GP, i.e. insert and shrink, can lead to recycling of the same structure across multiple generations. In order to avoid such repetitions, an archive called Hall of Shame is maintained during evolution, as illustrated in FIG. 10E. This archive consists of individuals representative of stagnated species, i.e. regions in the architecture space that have already been discovered by evolution but are no longer actively searched. During reproduction, new offsprings are repeatedly mutated until they result in an individual that does not belong to Hall of Shame. Mutations that lead to Hall of Shame are not discarded, but instead used as stepping stones to generate better individuals. Such memory based evolution is similar to novelty search. However, unlike novelty search, there is no additional fitness objective, simply an archive.

Evolution of a RNN Layer Having a Fixed Architecture

GP evolution of recurrent nodes starts with a simple fully connected tree. During the course of evolution, the tree size increases due to insert mutations and decreases due to shrink mutations. In an embodiment, a maximum possible height of the tree can be fixed at, for example, 15. However, there is no restriction on the maximum width of the tree. The search space for the nodes can be more varied and several orders of magnitude larger than in previous approaches. More specifically, main differences from the state-of-the-art Neural Architecture Search (NAS) are: (1) NAS searches for trees of fixed height 10 layers deep, whereas GP searches for trees with height varying between six (the size of fully connected simple tree) and 15 (a constraint added to GP); (2) Unlike in NAS, in GP different leaf elements can occur at varying depths in GP; and (3) NAS adds several constraint to the tree structure (e.g., a linear element in the tree is always followed by a non-linear element, whereas GP prevents only consecutive non-linearities (they would cause loss of information since the connections within a cell are not weighted); and (4) in NAS, inputs to the tree are used only once, whereas in GP, the inputs can be used multiple times within a node.

Most gated recurrent node architectures consist of a single native memory cell. This memory cell is the main reason why LSTMs perform better than simple RNNs. One key innovation introduced in this paper is to allow multiple native memory cells within a node. The memory cell output is fed back as input in the next time step without any modification, i.e. this recurrent loop is essentially a skip connection. Adding another memory cell in the node therefore does not affect the number of trainable parameters, it only adds to the representational power of the node.

Evolution of a RNN Layer Without a Fixed Architecture

Standard recurrent networks consist of layers formed by repetition of a single type of node. However, the search for better recurrent nodes through evolution often results in solutions with similar task performance but very different structure. Forming a recurrent layer by combining such diverse node solutions is potentially a powerful idea, related to the idea of ensembling, where different models are combined together to solve a task better. In an embodiment heterogenous recurrent networks are constructed by combining diverse evolved nodes into a layer, as illustrated in FIG. 10B. A candidate population is created that consists of top performing evolved nodes that are structurally very different from other nodes. The structure difference is calculated using the tree distance formula discussed above. Each heterogeneous layer is constructed by selecting nodes randomly from the candidate population. Each node is repeated 20 times in a layer; thus, if the layer size is e.g. 100, it can consist of five different node types, each of cardinality 20. The random search is an initial test of this idea.

Using Meta-LSTM for Fitness Prediction

In both node and network architecture search, it can about two hours to fully train a network until 40 epochs. With sufficient computing power it is possible to do it: for instance Zoph et al. used 800 GPUs for training multiple such solutions in parallel. However, if training time could be shortened, no matter what resources are available, those resources could be used better. A common strategy for such situations is early stopping, i.e. selecting networks based on partial training. For example in case of recurrent networks, the training time would be cut down to one validation loss instead of 40th. However, this is not a good strategy. Networks that train faster in the initial epochs often end up with a higher final loss. To overcome costly evaluation and to speed up evolution, a Meta-LSTM framework for fitness prediction has been developed. Meta-LSTM is a sequence to sequence model that consists of an encoder RNN and a decoder RNN. Validation perplexity of the first 10 epochs is provided as sequential input to the encoder, and the decoder is trained to predict the validation loss at epoch 40. Training data for these models is generated by fully training sample networks (i.e. until 40 epochs). The loss is the mean absolute error percentage at epoch 40. This error measure is used instead of mean squared error because it is unaffected by the magnitude of perplexity (poor networks can have very large perplexity values that overwhelm MSE). The hyperparameter values of the Meta-LSTM were selected based on its performance in the validation dataset. The best configuration that achieved an error rate of 3% includes an ensemble of two seq2seq models: one with a decoder length of 30 and the other with a decoder length of 1.

Note that Meta-LSTM is trained separately and only deployed for use during evolution. Thus, networks can be partially trained with a 4× speedup, and assessed with near-equal accuracy as with full training.

FIG. 11 illustrates various modules that can be used to implement the functionality of the training system 900 in FIG. 9. In particular, FIG. 11 shows a first evolution at the level of blueprints that comprise the supermodules and a second evolution at the level of supermodules. The first and second evolutions occur in parallel. In FIG. 11, solid lines indicate data flow and broken lines indicate control flow. The modules in FIG. 11 can be implemented in hardware or software, and need not be divided up in precisely the same blocks as shown in FIG. 11. Some can also be implemented on different processors or computers, or spread among a number of different processors or computers. In addition, it will be appreciated that some of the modules can be combined, operated in parallel or in a different sequence than that shown in FIG. 11 without affecting the functions achieved. Also as used herein, the term “module” can include “submodules”, which themselves can be considered herein to constitute modules. For example, modules 1108, 1118 and 1128 are also considered herein to be submodules of a competition processor. The blocks in FIG. 11 designated as modules can also be thought of as flowchart steps in a method.

FIG. 11 depicts multiple loops of evolution executing in parallel. A first loop of evolution operates at the blueprint level. A second loop of evolution operates at the supermodule level. Furthermore, speciation creates multiple mini-loops of evolution which also operate in parallel at each supermodule subpopulation level.

At the blueprint level, a blueprint instantiation module 1144 instantiates an initial population of blueprints and blueprint instantiations by inserting in respective supermodule slots supermodules from the supermodule subpopulations (e.g., subpopulations 1 to n) identified by the blueprints and the blueprint instantiations.

Then, a blueprint instantiation training module 1134 trains on training data 918 in FIG. 9 each of the blueprint instantiations. In particular, the neural network expressions of the supermodules inserted in each of the blueprint instantiations are trained. In one implementation, all the neural network modules in a supermodule are trained together. In another implementation, all the neural network supermodules in a blueprint instantiation are trained together.

Then, a blueprint instantiation testing module 1124 tests on validation data 928 in FIG. 9 (i.e., unseen test data) each of the blueprint instantiations. In particular, the neural network expressions of the supermodules inserted in each of the blueprint instantiations are tested. In one implementation, all the neural network modules in a supermodule are tested together. In another implementation, all the neural network supermodules in a blueprint instantiation are tested together.

Testing each of the blueprint instantiations results in a performance measure of each of the blueprint instantiations. A fitness calculator module 1114 uses this performance measure to update the fitness of the blueprints and the included supermodules. In one implementation, for a given blueprint, the fitness is calculated by taking an average of the respective fitnesses of corresponding blueprint instantiations of the given blueprint, as discussed above. So, if a particular blueprint had three blueprint instantiations which had respective performance measures of 30%, 40%, and 50%, then the particular blueprint's fitness will be the average of 30%, 40%, and 50%, i.e., 40%. In one implementation, for a given supermodule, the fitness is calculated by continuously assigning the given supermodule a fitness of the blueprint in which it is included, and taking an average of the respective fitnesses of all the blueprints in which the it was included, as discussed above. So, if a particular supermodule was included in three different blueprints which had respective performance measures of 10%, 20%, and 30%, then the particular supermodule's fitness will be the average of 10%, 20%, and 30%, i.e., 20%.

Once the fitness of the blueprints and the included supermodules is updated, the blueprints are sent to the blueprint competition module 1102 where certain low fitness blueprints are discarded, as discussed above. Following that, the blueprints that are not discarded are subject to procreation at the blueprint procreation module 1122, as discussed above. This is the first evolution loop at the blueprint level.

On the other hand, the included supermodules are sent to their respect subpopulations where they undergo competition and procreation only within their respective subpopulations. This is the second evolution loop at the subpopulation level and also creates multiple mini-loops of evolution at the level of each of the subpopulations. The second loop and the mini-loops of evolution are depicted in FIG. 11 using supermodule subpopulation 1, 2, and 3 competition modules 1108, 1118, and 1128 and supermodule subpopulation 1, 2, and 3 procreation modules 1138, 1148, and 1158. After procreation, the supermodules undergo speciation at the speciation module 1168 to create new and modified subpopulations, as discussed above.

The new and modified subpopulations are then again used to instantiate blueprints coming out of the blueprint procreation module 1122. The process continues until a convergence condition is met, as discussed above.

The method described in this section and other sections of the technology disclosed can include one or more of the following features and/or features described in connection with additional methods disclosed. In the interest of conciseness, the combinations of features disclosed in this application are not individually enumerated and are not repeated with each base set of features. The reader will understand how features identified in this method can readily be combined with sets of base features identified as implementations such as terminology, system overview, supermodules, blueprints, blueprint fitness, supermodule fitness, training system, example results, client-server architectures, computer system, and claims.

Other implementations of the method described in this section can include a computer readable storage medium storing instructions in a non-transitory manner, which are executable by a processor to perform any of the methods described above. Yet another implementation of the method described in this section can include a system including memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above.

Client-Server Architecture

In this section, the term “genome” is used to equivalently refer to both a blueprint and a supermodule. In some environments, the training data used to evaluate a genome's fitness can be voluminous. Therefore, even with modern high processing power and large memory capacity computers, achieving quality results within a reasonable time is often not feasible on a single machine. A large module pool also requires a large memory and high processing power. In one implementation, therefore, a client/server model is used to provide scaling in order to achieve high quality evaluation results within a reasonable time period. Scaling is carried out in two dimensions, namely in pool size as well as in evaluation of the same genome to generate a more diverse module pool so as to increase the probability of finding fitter genomes. In the client/server implementation, the genome pool is distributed over a multitude of clients for evaluation. Each client continues to evaluate its own client-centric module pool using data from training database 918 of FIG. 9, which it may receive in bulk or periodically on a sustained and continuing basis. Genomes that satisfy one or more predefined conditions on a client computer are transmitted to the server to form part of a server-centric module pool.

Distributed processing of genomes also may be used to increase the speed of evaluation of a given genome. To achieve this, genomes that are received by the server but have not yet been tested on a certain number of samples, or have not yet met one or more predefined conditions, may be sent back from the server to a multitude of clients for further evaluation. The evaluation result achieved by the clients (alternatively called herein as partial evaluation) for a genome is transferred back to the server. The server merges the partial evaluation results of a genome with that genome's fitness estimate at the time it was sent to the clients to arrive at an updated fitness estimate for that genome in the server-centric module pool. For example, assume that a genome has been tested on 500 samples and is sent from the server to, for example, two clients each instructed to test the genome on 100 additional samples. Accordingly, each client further tests the genome on the additional 100 samples and reports its own client-centric fitness estimate to the server. The server combines these two estimates with the genome's fitness estimate at the time it was sent to the two clients to calculate an updated server-centric fitness estimate for the genome. The combined results represent the genome's fitness evaluated over 700 samples. In other words, the distributed system, in accordance with this example, increases the experience level of a genome from 500 samples to 700 samples using only 100 different training samples at each client. A distributed system, in accordance with the technology disclosed, is thus highly scalable in evaluating its genomes.

Advantageously, clients are enabled to perform genome procreation locally, thereby improving the quality of their genomes. Each client is a self-contained evolution device, not only evaluating one or more genomes in its own pool at a time, but also creating a new generation of genomes and moving the evolutionary process forward locally. Thus clients maintain their own client-centric module pool which need not match each other's or the server-centric module pool. Since the clients continue to advance with their own local evolutionary process, their processing power is not wasted even if they are not in constant communication with the server. Once communication is reestablished with the server, clients can send in their fittest genomes to the server and receive additional genomes from the server for further testing.

In yet another implementation, the entire evolution process in not distributed across multiple clients, and only the training and testing, i.e., evaluation, of the genomes is distributed across multiple clients (e.g., each network can be trained and tested on a different client).

Particular Implementations

We describe systems, methods, and articles of manufacture for cooperatively evolving a deep neural network structure. One or more features of an implementation can be combined with the base implementation. Implementations that are not mutually exclusive are taught to be combinable. One or more features of an implementation can be combined with other implementations. This disclosure periodically reminds the user of these options. Omission from some implementations of recitations that repeat these options should not be taken as limiting the combinations taught in the preceding sections—these recitations are hereby incorporated forward by reference into each of the following implementations.

A system implementation of the technology disclosed includes one or more processors coupled to the memory. The memory is loaded with computer instructions which, when executed on the processors, cause cooperative evolution of a deep neural network structure that solves a provided problem when trained on a source of training data containing labeled examples of data sets for the problem.

The deep neural network structure includes a plurality of modules and interconnections among the modules. Examples of deep neural network structures include:

-   -   AlexNet     -   ResNet     -   Inception     -   WaveNet     -   PixelCNN     -   GoogLeNet     -   ENet     -   U-Net     -   BN-NIN     -   VGG     -   LeNet     -   Deep SEA     -   DeepChem     -   DeepBind     -   DeepMotif     -   FIDDLE     -   DeepLNC     -   DeepCpG     -   DeepCyTOF     -   SPINDLE

The memory stores a candidate supermodule genome database that contains a pool of candidate supermodules. Each of the candidate supermodules identify respective values for a plurality of supermodule hyperparameters of the supermodule. The supermodule hyperparameters include supermodule global topology hyperparameters that identify a plurality of modules in the candidate supermodule and module interconnects among the modules in the candidate supermodule. At least one of the modules in each candidate supermodule includes a neural network. Each candidate supermodule has associated therewith storage for an indication of a respective supermodule fitness value.

The memory further stores a blueprint genome database that contains a pool of candidate blueprints for solving the provided problem. Each of the candidate blueprints identify respective values for a plurality of blueprint topology hyperparameters of the blueprint. The blueprint topology hyperparameters include a number of included supermodules and interconnects among the included supermodules. Each candidate blueprint has associated therewith storage for an indication of a respective blueprint fitness value.

The system includes an instantiation module. The instantiation module instantiates each of at least a training subset of the blueprints in the pool of candidate blueprints. At least one of the blueprints is instantiated more than once. Each instantiation of a candidate blueprint includes identifying for the instantiation a supermodule from the pool of candidate supermodules for each of the supermodules identified in the blueprint.

The system includes a training module. The training module trains neural networks on training data from the source of training data. The neural networks are modules which are identified by supermodules in each of the blueprint instantiations. The training includes modifying submodules of the neural network modules in dependence upon back-propagation algorithms.

The system includes an evaluation module. For each given one of the blueprints in the training subset of blueprints, the evaluation module evaluates each instantiation of the given blueprint on validation data to develop a blueprint instantiation fitness value associated with each of the blueprint instantiations. The validation data can be data previously unseen during training of a particular supermodule. For each given one of the blueprints in the training subset of blueprints, the evaluation module updates fitness values of all supermodules identified for inclusion in each instantiation of the given blueprint in dependence upon the fitness value of the blueprint instantiation. For each given one of the blueprints in the training subset of blueprints, the evaluation module updates a blueprint fitness value for the given blueprint in dependence upon the fitness values for the instantiations of the blueprint.

The system includes a competition module. The competition module selects blueprints for discarding from the pool of candidate blueprints in dependence upon their updated fitness values. The competition module then selects supermodules from the candidate supermodule pool for discarding in dependence upon their updated fitness values.

The system includes a procreation module. The procreation module forms new supermodules in dependence upon a respective set of at least one parent supermodule from the pool of candidate supermodules. The procreation module also forms new blueprints in dependence upon a respective set of at least one parent blueprint from the pool of candidate blueprints.

The system includes a solution harvesting module. The solution harvesting module provides for deployment a selected one of the blueprints remaining in the candidate blueprint pool, instantiated with supermodules selected from the candidate supermodule pool.

This system implementation and other systems disclosed optionally include one or more of the following features. System can also include features described in connection with methods disclosed. In the interest of conciseness, alternative combinations of system features are not individually enumerated. Features applicable to systems, methods, and articles of manufacture are not repeated for each statutory class set of base features. The reader will understand how features identified in this section can readily be combined with base features in other statutory classes.

Each supermodule in the pool of candidate supermodules further belongs to a subpopulation of the supermodules.

The blueprint topology hyperparameters of blueprints in the pool of candidate blueprints can also identify a supermodule subpopulation for each included supermodule.

The instantiation module can select, for each supermodule identified in the blueprint, a supermodule from the subpopulation of supermodules which is identified by the blueprint.

The competition module, in selecting supermodules from the candidate supermodule pool for discarding in dependence upon their updated fitness values, can do so in further dependence upon the subpopulation to which the supermodules belong.

The procreation module, in forming new supermodules in dependence upon a respective set of at least one parent supermodule from the pool of candidate supermodules, can form the new supermodules only in dependence upon parent supermodules which belong to the same subpopulation.

The system can be configured to further comprise a re-speciation module which re-speciates the supermodules in the pool of candidate supermodules into updated subpopulations.

The competition module can select supermodules for discarding from the subpopulation with a same subpopulation identifier (ID).

The system can be configured to further comprise a control module which invokes, for each of a plurality of generations, the training module, the evaluation module, the competition module, and the procreation module.

A particular supermodule can be identified in a plurality of blueprint instantiations. The evaluation module can update a supermodule fitness value associated with the particular supermodule in dependence of respective blueprint instantiation fitness values associated with each of the blueprint instantiations in the plurality.

The supermodule fitness value can be an average of the respective blueprint instantiation fitness values. The evaluation module can assign a supermodule fitness value to a particular supermodule if the supermodule fitness value is previously undetermined.

The evaluation module, for a particular supermodule, can merge a current supermodule fitness value with a previously determined supermodule fitness. The merging can include averaging.

The evaluation module can update the blueprint fitness value for the given blueprint by averaging the fitness values for the instantiations of the blueprint.

The supermodule hyperparameters can further comprise module topology hyperparameters that identify a plurality of submodules of the neural network and interconnections among the submodules. Crossover and mutation of the module topology hyperparameters during procreation can include modifying a number of submodules and/or interconnections among them.

Other implementations may include a non-transitory computer readable storage medium storing instructions executable by a processor to perform actions of the system described above.

A computer-implemented method implementation of the technology disclosed includes cooperatively evolving a deep neural network structure that solves a provided problem when trained on a source of training data containing labeled examples of data sets for the problem.

The deep neural network structure includes a plurality of modules and interconnections among the modules. Examples of deep neural network structures include:

-   -   AlexNet     -   ResNet     -   Inception     -   WaveNet     -   PixelCNN     -   GoogLeNet     -   ENet     -   U-Net     -   BN-NIN     -   VGG     -   LeNet     -   Deep SEA     -   DeepChem     -   DeepBind     -   DeepMotif     -   FIDDLE     -   DeepLNC     -   DeepCpG     -   DeepCyTOF     -   SPINDLE

The method includes storing a candidate supermodule genome database that contains a pool of candidate supermodules. Each of the candidate supermodules identify respective values for a plurality of supermodule hyperparameters of the supermodule. The supermodule hyperparameters include supermodule global topology hyperparameters that identify a plurality of modules in the candidate supermodule and module interconnects among the modules in the candidate supermodule. At least one of the modules in each candidate supermodule includes a neural network. Each candidate supermodule has associated therewith storage for an indication of a respective supermodule fitness value.

The method includes storing a blueprint genome database that contains a pool of candidate blueprints for solving the provided problem. Each of the candidate blueprints identify respective values for a plurality of blueprint topology hyperparameters of the blueprint. The blueprint topology hyperparameters include a number of included supermodules and interconnects among the included supermodules. Each candidate blueprint has associated therewith storage for an indication of a respective blueprint fitness value.

The method includes instantiating each of at least a training subset of the blueprints in the pool of candidate blueprints. At least one of the blueprints is instantiated more than once. Each instantiation of a candidate blueprint includes identifying for the instantiation a supermodule from the pool of candidate supermodules for each of the supermodules identified in the blueprint.

The method includes training neural networks on training data from the source of training data. The neural networks are modules which are identified by supermodules in each of the blueprint instantiations. The training further includes modifying submodules of the neural network modules in dependence upon back-propagation algorithms.

For each given one of the blueprints in the training subset of blueprints, the method includes evaluating each instantiation of the given blueprint on validation data to develop a blueprint instantiation fitness value associated with each of the blueprint instantiations. The validation data can be data previously unseen during training of a particular supermodule. For each given one of the blueprints in the training subset of blueprints, the method includes updating fitness values of all supermodules identified for inclusion in each instantiation of the given blueprint in dependence upon the fitness value of the blueprint instantiation. For each given one of the blueprints in the training subset of blueprints, the method includes updating a blueprint fitness value for the given blueprint in dependence upon the fitness values for the instantiations of the blueprint.

The method includes selecting blueprints for discarding from the pool of candidate blueprints in dependence upon their updated fitness values and then selecting supermodules from the candidate supermodule pool for discarding in dependence upon their updated fitness values.

The method includes forming new supermodules in dependence upon a respective set of at least one parent supermodule from the pool of candidate supermodules and forming new blueprints in dependence upon a respective set of at least one parent blueprint from the pool of candidate blueprints.

The method includes deploying a selected one of the blueprints remaining in the candidate blueprint pool, instantiated with supermodules selected from the candidate supermodule pool.

Each of the features discussed in this particular implementation section for the system implementation apply equally to this method implementation. As indicated above, all the system features are not repeated here and should be considered repeated by reference.

Other implementations may include a non-transitory computer readable storage medium (CRM) storing instructions executable by a processor to perform the method described above. Yet another implementation may include a system including memory and one or more processors operable to execute instructions, stored in the memory, to perform the method described above.

Computer System

FIG. 12 is a simplified block diagram of a computer system 1200 that can be used to implement either or both of the training system and the production system of the technology disclosed. Computer system 1200 includes at least one central processing unit (CPU) 1224 that communicates with a number of peripheral devices via bus subsystem 1222. These peripheral devices can include a storage subsystem 1210 including, for example, memory devices and a file storage subsystem 1218, user interface input devices 1220, user interface output devices 1228, and a network interface subsystem 1226. The input and output devices allow user interaction with computer system 1200. Network interface subsystem 1226 provides an interface to outside networks, including an interface to corresponding interface devices in other computer systems.

In one implementation, the training system 900 in FIG. 9 and/or the production system 934 in FIG. 9 are communicably linked to the storage subsystem 1210 and a user interface input devices 1220.

User interface input devices 1220 can include a keyboard; pointing devices such as a mouse, trackball, touchpad, or graphics tablet; a scanner; a touch screen incorporated into the display; audio input devices such as voice recognition systems and microphones; and other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and ways to input information into computer system 1200.

User interface output devices 1228 can include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem can include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a visible image. The display subsystem can also provide a non-visual display such as audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computer system 1200 to the user or to another machine or computer system.

Storage subsystem 1210 stores programming and data constructs that provide the functionality of some or all of the modules and methods described herein. These software modules are generally executed by deep learning processors 1230.

Deep learning processors 1230 can be graphics processing units (GPUs) or field-programmable gate arrays (FPGAs). Deep learning processors 1230 can be hosted by a deep learning cloud platform such as Google Cloud Platform™, Xilinx™, and Cirrascale™. Examples of deep learning processors 1230 include Google's Tensor Processing Unit (TPU)™, rackmount solutions like GX4 Rackmount Series™, GX8 Rackmount Series™, NVIDIA DGX-1™, Microsoft' Stratix V FPGA™, Graphcore's Intelligent Processor Unit (IPU)™, Qualcomm's Zeroth Platform™ with Snapdragon Processors™, NVIDIA's Volta™, NVIDIA's DRIVE PX™, NVIDIA's JETSON TX1/TX2 MODULE™, Intel's Nirvana™, Movidius VPU™, Fujitsu DPI™, ARM's DynamiclQ™, IBM TrueNorth™, and others.

Memory subsystem 1212 used in the storage subsystem 1210 can include a number of memories including a main random-access memory (RAM) 1214 for storage of instructions and data during program execution and a read only memory (ROM) 1216 in which fixed instructions are stored. A file storage subsystem 1218 can provide persistent storage for program and data files, and can include a hard disk drive, a floppy disk drive along with associated removable media, a CD-ROM drive, an optical drive, or removable media cartridges. The modules implementing the functionality of certain implementations can be stored by file storage subsystem 1218 in the storage subsystem 1210, or in other machines accessible by the processor.

Bus subsystem 1222 provides a mechanism for letting the various components and subsystems of computer system 1200 communicate with each other as intended. Although bus subsystem 1222 is shown schematically as a single bus, alternative implementations of the bus subsystem can use multiple busses.

Computer system 1200 itself can be of varying types including a personal computer, a portable computer, a workstation, a computer terminal, a network computer, a television, a mainframe, a server farm, a widely-distributed set of loosely networked computers, or any other data processing system or user device. Due to the ever-changing nature of computers and networks, the description of computer system 1200 depicted in FIG. 12 is intended only as a specific example for purposes of illustrating the preferred embodiments. Many other configurations of computer system 1200 are possible having more or less components than the computer system depicted in FIG. 12.

The preceding description is presented to enable the making and use of the technology disclosed. Various modifications to the disclosed implementations will be apparent, and the general principles defined herein may be applied to other implementations and applications without departing from the spirit and scope of the technology disclosed. Thus, the technology disclosed is not intended to be limited to the implementations shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. The scope of the technology disclosed is defined by the appended claims. 

1. A computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem, the system comprising: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting a candidate RNN node from the pool of candidate RNN nodes, the candidate RNN node being selected such that each respective RNN layer includes the selected candidate RNN node, N being a predetermined integer that is greater than 1, and replicating the selected candidate RNN node within each respective RNN layer M−1 times, such that each respective RNN layer includes M candidate RNN nodes, M being a predetermined integer that is greater than 1; an evolution module that evolves, for each respective RNN layer, the M candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the M candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.
 2. A computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem, the system comprising: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting a candidate RNN node from the pool of candidate RNN nodes, the candidate RNN nodes being selected such that each respective RNN layer includes the selected candidate RNN node, N being a predetermined integer that is greater than 1, and replicating the selected candidate RNN node within each respective RNN layer M−1 times, such that each respective RNN layer includes M candidate RNN nodes, M being a predetermined integer that is greater than 1; an evolution module that evolves, for each respective RNN layer, the M candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the M candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that: evaluates a performance of each candidate RNN node of each layer using validation data for X epochs to obtain perplexity of each candidate RNN node at X epochs, X being an integer that is greater than 1, implements one or more long short-term memory (LSTM) neural network trained to predict perplexity of each candidate RNN node at Y epochs, given the obtained perplexity at X epochs as an input, Y being an integer and at least 4 times greater than X, and assigns a fitness value to each candidate RNN node in dependence upon the predicted perplexity; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.
 3. A computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem, the system comprising: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting, for each respective RNN layer of the N RNN layers, a predetermined number H of candidate RNN nodes from the pool of candidate RNN nodes, the H candidate RNN nodes being selected such that each of the H candidate RNN nodes has a unique structure, N being a predetermined integer that is greater than 1, and replicating the H candidate RNN nodes within each respective RNN layer a certain number of times, such that each RNN layer includes the same number of candidate RNN nodes; an evolution module that evolves, for each respective RNN layer, the H candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the H candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.
 4. A computer-implemented system for evolving a recurrent neural network (RNN) that solves a provided problem, the system comprising: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes identifying respective values for a plurality of hyperparameters of the RNN node and each of the candidate RNN nodes representing a neural network as a tree structure, each candidate RNN node having a unique tree structure with respect to the other candidate RNN nodes of the pool of candidate RNN nodes; an assembly module that assembles N RNN layers by: selecting, for each respective RNN layer of the N RNN layers, a predetermined number of H candidate RNN node from the pool of candidate RNN nodes, the H candidate RNN nodes being selected such that each of the H candidate RNN nodes has a unique structure, N being a predetermined integer that is greater than 1, and replicating the H candidate RNN node within each respective RNN layer a certain number of times, such that each RNN layer includes the same number of candidate RNN nodes; an evolution module that evolves, for each respective RNN layer, the H candidate RNN nodes G times, G being a predetermined integer that is greater than 1, the H candidate RNN nodes of each respective RNN layer being evolved by: performing speciation and crossover at a probability of sc and according to a speciation compatibility threshold of cth, performing an insert mutation at a probability of im, performing a shrink mutation at a probability of sm, and performing a replacement mutation at a probability of rm; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that: evaluates a performance of each candidate RNN node of each RNN layer using validation data for X epochs to obtain perplexity of each candidate RNN node at X epochs, X being an integer that is greater than 1, implements one or more long short-term memory (LSTM) neural network trained to predict perplexity of each candidate RNN node at Y epochs, given the obtained perplexity at X epochs as an input, Y being an integer and at least 4 times greater than X, and assigns a fitness value to each candidate RNN node in dependence upon the predicted perplexity; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool. 